The rapid development of deep learning (DL) and widespread applications of
Internet-of-Things (IoT) have made the devices smarter than before, and enabled
them to perform more intelligent tasks. However, it is challenging for any IoT
device to train and run DL models independently due to its limited computing
capability. In this paper, we consider an IoT network where the cloud/edge
platform performs the DL based semantic communication (DeepSC) model training
and updating while IoT devices perform data collection and transmission based
on the trained model. To make it affordable for IoT devices, we propose a lite
distributed semantic communication system based on DL, named L-DeepSC, for text
transmission with low complexity, where the data transmission from the IoT
devices to the cloud/edge works at the semantic level to improve transmission
efficiency. Particularly, by pruning the model redundancy and lowering the
weight resolution, the L-DeepSC becomes affordable for IoT devices and the
bandwidth required for model weight transmission between IoT devices and the
cloud/edge is reduced significantly. Through analyzing the effects of fading
channels in forward-propagation and back-propagation during the training of
L-DeepSC, we develop a channel state information (CSI) aided training
processing to decrease the effects of fading channels on transmission.
Meanwhile, we tailor the semantic constellation to make it implementable on
capacity-limited IoT devices. Simulation demonstrates that the proposed
L-DeepSC achieves competitive performance compared with traditional methods,
especially in the low signal-to-noise (SNR) region. In particular, while it can
reach as large as 40x compression ratio without performance degradation.Comment: Accpeted by JSA
